Enormous advances have been made in digital computing over the past 70 years. Crude, high-energy-consuming, vacuum-tube-based computer systems developed in the 1940s have evolved into today's personal computers, work stations, servers, and high-end distributed computer systems, based on multi-core single-integrated-circuit processors, that economically provide processing speeds, data-storage capacities, and data-transfer bandwidths that were unimaginable even 20 years ago. However, digital computing appears to be bounded by certain physical and problem-domain constraints.
With regard to physical constraints, processing speeds and data-storage capacities are generally inversely related to the minimum sizes at which transistors and other circuit elements can be fabricated within integrated circuits. Much of the exponential growth observed in computational bandwidth for various classes of computer systems can be attributed to a corresponding decrease in feature sizes within integrated circuits. There are, however, fundamental physical limits, on the order of the sizes of complex molecules, past which feature sizes cannot be further decreased, and somewhat larger feature-size limitations past which further decreases in feature sizes can be obtained only by exponentially increasing integrated-circuit cost.
With regard to problem-domain constraints, while digital computers provide the basis for practical and cost-effective solutions of many types of computational problems, there are many types and classes of computational problems that appear incapable of being addressed efficiently by digital computer systems. Examples include accurate simulation of the quantum-mechanical behavior of large molecules and aggregations of molecules and a variety of traditional numerical and computational problems, including large-integer factoring and graph-isomorphism problems.
In 1982, Richard Feynman made a suggestion for a new type of computational system based on quantum-mechanical components. He suggested that quantum computers could more efficiently address certain classes of computational problems than digital computers and, in the case of computational problems in computational chemistry and physics, provide practical approaches to computational problems that are intractable using digital computer systems. Since that time, great progress has been made in developing the theoretical foundation for quantum computing and the first quantum computers have been implemented. Various computational problems have been identified that can be addressed more efficiently by quantum computers than by classical digital computers. However, significant research and development efforts continue to be applied in order to provide practical and cost-effective general-purpose quantum-computing systems for widespread use. As one example, significant theoretical efforts are currently being applied to identify practical and cost-effective implementations of quantum circuits.